The range of a dataset is calculated by subtracting the minimum value from the maximum value. This measures the spread of data from the smallest to the largest value, making option A the correct answer.

The main challenge addressed by the transformer architecture is capturing long-range dependencies in sequential data. Transformers use self-attention mechanisms to understand the relationship between distant words in a sentence, making them effective for various NLP tasks like machine translation and text summarization.

Relational databases are designed for storing structured data with well-defined rows and columns. This structured format allows for efficient storage and querying of data. Unstructured data, on the other hand, lacks a predefined structure.

To prevent SQL injection, you should sanitize and parameterize user inputs in your queries. This involves validating and escaping user input data to ensure that it cannot be used to execute malicious SQL commands. Other options, while important, do not directly prevent SQL injection.

NoSQL databases are schemaless, which means they do not require a fixed schema for data storage. This flexibility allows for the storage of various types of data without predefined structure constraints.

Talend is an ETL (Extract, Transform, Load) tool known for providing native integrations with Apache Hadoop, Apache Spark, and other big data technologies. This makes it a popular choice for organizations dealing with big data workloads, as it allows for efficient data extraction and processing from these technologies within the ETL pipeline. Other tools mentioned do not offer the same level of native integration with big data technologies.

Unsupervised Learning with K-Means Clustering is suitable for customer segmentation as it groups customers based on similarities in credit card usage behavior without predefined labels. Supervised learning requires labeled data, reinforcement learning is used for sequential decision-making, and semi-supervised learning combines labeled and unlabeled data.

Semi-structured data is a type of data that falls between structured and unstructured data. It can often be represented as a combination of structured tables with additional metadata or annotations. This format provides some level of organization and makes it more manageable for analysis. Examples of semi-structured data include JSON, XML, and log files, which have some inherent structure but may also contain unstructured elements.

Real-time data processing is commonly referred to as "Stream Processing." In this approach, data is processed as it is generated, allowing for real-time analysis and decision-making. It is crucial in applications where immediate insights or actions are required.

The Star Schema is a common data warehousing schema where a central fact table stores quantitative data, and dimension tables provide context and details about the data. This schema simplifies querying and reporting.